{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\analysis_2.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Nov 2024, 13:18:05

{com}. meoprobit my_edu class education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,946
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        16
{col 63}{txt}avg{col 67}={res}{col 69}     380.8
{col 63}{txt}max{col 67}={res}{col 69}     1,014

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   185.41
{txt}Log likelihood = {res}-15168.431{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0116668{col 26}{space 2} .0062831{col 37}{space 1}    1.86{col 46}{space 3}0.063{col 54}{space 4}-.0006479{col 67}{space 3} .0239815
{txt}{space 3}education {c |}{col 14}{res}{space 2} .0864888{col 26}{space 2} .0078935{col 37}{space 1}   10.96{col 46}{space 3}0.000{col 54}{space 4} .0710178{col 67}{space 3} .1019598
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0091949{col 26}{space 2} .0065055{col 37}{space 1}    1.41{col 46}{space 3}0.158{col 54}{space 4}-.0035556{col 67}{space 3} .0219455
{txt}{space 6}gender {c |}{col 14}{res}{space 2}  .053027{col 26}{space 2} .0194009{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} .0150019{col 67}{space 3} .0910521
{txt}{space 5}marital {c |}{col 14}{res}{space 2} -.006097{col 26}{space 2} .0199609{col 37}{space 1}   -0.31{col 46}{space 3}0.760{col 54}{space 4}-.0452196{col 67}{space 3} .0330257
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0577125{col 26}{space 2} .0221412{col 37}{space 1}    2.61{col 46}{space 3}0.009{col 54}{space 4} .0143165{col 67}{space 3} .1011084
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0056682{col 26}{space 2} .0082956{col 37}{space 1}    0.68{col 46}{space 3}0.494{col 54}{space 4}-.0105909{col 67}{space 3} .0219274
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2253426{col 26}{space 2} .0293399{col 37}{space 1}   -7.68{col 46}{space 3}0.000{col 54}{space 4}-.2828478{col 67}{space 3}-.1678374
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.073608{col 26}{space 2}  .086556{col 37}{space 1}  -23.96{col 46}{space 3}0.000{col 54}{space 4}-2.243254{col 67}{space 3}-1.903961
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.317857{col 26}{space 2} .0822888{col 37}{space 1}  -16.02{col 46}{space 3}0.000{col 54}{space 4} -1.47914{col 67}{space 3}-1.156574
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.2075206{col 26}{space 2} .0813475{col 37}{space 1}   -2.55{col 46}{space 3}0.011{col 54}{space 4}-.3669588{col 67}{space 3}-.0480824
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.162483{col 26}{space 2} .0817265{col 37}{space 1}   14.22{col 46}{space 3}0.000{col 54}{space 4} 1.002302{col 67}{space 3} 1.322664
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1140384{col 26}{space 2} .0293536{col 54}{space 4} .0688574{col 67}{space 3}  .188865
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1107.61{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,605
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     341.3
{col 63}{txt}max{col 67}={res}{col 69}       948

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   183.11
{txt}Log likelihood = {res}-13509.277{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0105708{col 26}{space 2} .0067856{col 37}{space 1}    1.56{col 46}{space 3}0.119{col 54}{space 4}-.0027288{col 67}{space 3} .0238703
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0120181{col 26}{space 2} .0103597{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4}-.0082866{col 67}{space 3} .0323227
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .088611{col 26}{space 2} .0086542{col 37}{space 1}   10.24{col 46}{space 3}0.000{col 54}{space 4} .0716492{col 67}{space 3} .1055729
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0105183{col 26}{space 2} .0068824{col 37}{space 1}    1.53{col 46}{space 3}0.126{col 54}{space 4} -.002971{col 67}{space 3} .0240075
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0527787{col 26}{space 2} .0210034{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0116129{col 67}{space 3} .0939446
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0084019{col 26}{space 2} .0211909{col 37}{space 1}   -0.40{col 46}{space 3}0.692{col 54}{space 4}-.0499352{col 67}{space 3} .0331314
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0519691{col 26}{space 2} .0233491{col 37}{space 1}    2.23{col 46}{space 3}0.026{col 54}{space 4} .0062057{col 67}{space 3} .0977325
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0114149{col 26}{space 2} .0088005{col 37}{space 1}    1.30{col 46}{space 3}0.195{col 54}{space 4}-.0058337{col 67}{space 3} .0286635
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2270885{col 26}{space 2} .0306396{col 37}{space 1}   -7.41{col 46}{space 3}0.000{col 54}{space 4}-.2871411{col 67}{space 3}-.1670359
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.064745{col 26}{space 2} .0917349{col 37}{space 1}  -22.51{col 46}{space 3}0.000{col 54}{space 4}-2.244542{col 67}{space 3}-1.884948
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.281432{col 26}{space 2} .0867686{col 37}{space 1}  -14.77{col 46}{space 3}0.000{col 54}{space 4}-1.451495{col 67}{space 3}-1.111368
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.1514452{col 26}{space 2} .0858069{col 37}{space 1}   -1.76{col 46}{space 3}0.078{col 54}{space 4}-.3196237{col 67}{space 3} .0167332
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.229705{col 26}{space 2} .0862529{col 37}{space 1}   14.26{col 46}{space 3}0.000{col 54}{space 4} 1.060652{col 67}{space 3} 1.398758
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1190548{col 26}{space 2} .0308469{col 54}{space 4} .0716477{col 67}{space 3}   .19783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1012.21{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==2||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,605
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}        12
{col 63}{txt}avg{col 67}={res}{col 69}     341.3
{col 63}{txt}max{col 67}={res}{col 69}       948

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   189.50
{txt}Log likelihood = {res}-13506.053{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0090983{col 26}{space 2} .0068191{col 37}{space 1}    1.33{col 46}{space 3}0.182{col 54}{space 4}-.0042669{col 67}{space 3} .0224635
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0066642{col 26}{space 2} .0105831{col 37}{space 1}    0.63{col 46}{space 3}0.529{col 54}{space 4}-.0140784{col 67}{space 3} .0274067
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0239958{col 26}{space 2} .0660819{col 37}{space 1}   -0.36{col 46}{space 3}0.717{col 54}{space 4} -.153514{col 67}{space 3} .1055224
{txt}high_skilled {c |}{col 14}{res}{space 2} .0625352{col 26}{space 2} .0247844{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4} .0139586{col 67}{space 3} .1111117
{txt}{space 3}education {c |}{col 14}{res}{space 2}  .079327{col 26}{space 2}  .009406{col 37}{space 1}    8.43{col 46}{space 3}0.000{col 54}{space 4} .0608915{col 67}{space 3} .0977624
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0087872{col 26}{space 2}   .00692{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4}-.0047757{col 67}{space 3} .0223501
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0485045{col 26}{space 2} .0210801{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0071882{col 67}{space 3} .0898207
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0092592{col 26}{space 2} .0211951{col 37}{space 1}   -0.44{col 46}{space 3}0.662{col 54}{space 4}-.0508009{col 67}{space 3} .0322824
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0531815{col 26}{space 2} .0233577{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4} .0074011{col 67}{space 3} .0989618
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0106363{col 26}{space 2} .0088071{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-.0066252{col 67}{space 3} .0278979
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2236738{col 26}{space 2} .0307067{col 37}{space 1}   -7.28{col 46}{space 3}0.000{col 54}{space 4}-.2838579{col 67}{space 3}-.1634896
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-2.103301{col 26}{space 2} .0930514{col 37}{space 1}  -22.60{col 46}{space 3}0.000{col 54}{space 4}-2.285678{col 67}{space 3}-1.920923
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.319762{col 26}{space 2} .0881439{col 37}{space 1}  -14.97{col 46}{space 3}0.000{col 54}{space 4}-1.492521{col 67}{space 3}-1.147003
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}-.1891635{col 26}{space 2} .0871545{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.3599832{col 67}{space 3}-.0183437
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.192377{col 26}{space 2} .0875625{col 37}{space 1}   13.62{col 46}{space 3}0.000{col 54}{space 4} 1.020758{col 67}{space 3} 1.363996
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1194079{col 26}{space 2} .0309351{col 54}{space 4} .0718639{col 67}{space 3}  .198406
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1010.49{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_3

. meoprobit my_edu class education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    11,582
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         8
{col 63}{txt}avg{col 67}={res}{col 69}     340.6
{col 63}{txt}max{col 67}={res}{col 69}     1,204

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}8{txt}){col 67}={res}{col 70}   312.46
{txt}Log likelihood = {res}-13524.133{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0354691{col 26}{space 2} .0069497{col 37}{space 1}    5.10{col 46}{space 3}0.000{col 54}{space 4} .0218479{col 67}{space 3} .0490902
{txt}{space 3}education {c |}{col 14}{res}{space 2}   .11144{col 26}{space 2} .0085891{col 37}{space 1}   12.97{col 46}{space 3}0.000{col 54}{space 4} .0946056{col 67}{space 3} .1282744
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0282588{col 26}{space 2} .0068966{col 37}{space 1}    4.10{col 46}{space 3}0.000{col 54}{space 4} .0147417{col 67}{space 3} .0417759
{txt}{space 6}gender {c |}{col 14}{res}{space 2}   .02262{col 26}{space 2} .0203949{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0173533{col 67}{space 3} .0625933
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0322799{col 26}{space 2} .0217983{col 37}{space 1}   -1.48{col 46}{space 3}0.139{col 54}{space 4}-.0750037{col 67}{space 3} .0104439
{txt}{space 4}religion {c |}{col 14}{res}{space 2}-.0010707{col 26}{space 2}  .026565{col 37}{space 1}   -0.04{col 46}{space 3}0.968{col 54}{space 4}-.0531371{col 67}{space 3} .0509957
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0286093{col 26}{space 2} .0089311{col 37}{space 1}    3.20{col 46}{space 3}0.001{col 54}{space 4} .0111046{col 67}{space 3}  .046114
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2531403{col 26}{space 2} .0287527{col 37}{space 1}   -8.80{col 46}{space 3}0.000{col 54}{space 4}-.3094946{col 67}{space 3} -.196786
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.896859{col 26}{space 2} .0903398{col 37}{space 1}  -21.00{col 46}{space 3}0.000{col 54}{space 4}-2.073922{col 67}{space 3}-1.719796
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.117581{col 26}{space 2} .0857287{col 37}{space 1}  -13.04{col 46}{space 3}0.000{col 54}{space 4}-1.285606{col 67}{space 3}-.9495562
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2}  .084412{col 26}{space 2} .0849568{col 37}{space 1}    0.99{col 46}{space 3}0.320{col 54}{space 4}-.0821003{col 67}{space 3} .2509244
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.464937{col 26}{space 2} .0856298{col 37}{space 1}   17.11{col 46}{space 3}0.000{col 54}{space 4} 1.297106{col 67}{space 3} 1.632768
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1217915{col 26}{space 2} .0313146{col 54}{space 4} .0735799{col 67}{space 3} .2015926
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 1045.13{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,301
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     303.0
{col 63}{txt}max{col 67}={res}{col 69}     1,064

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}9{txt}){col 67}={res}{col 70}   317.35
{txt}Log likelihood = {res}-11957.587{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2}  .037892{col 26}{space 2} .0075062{col 37}{space 1}    5.05{col 46}{space 3}0.000{col 54}{space 4} .0231801{col 67}{space 3} .0526039
{txt}{space 6}income {c |}{col 14}{res}{space 2}  .002195{col 26}{space 2} .0107126{col 37}{space 1}    0.20{col 46}{space 3}0.838{col 54}{space 4}-.0188013{col 67}{space 3} .0231914
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1233371{col 26}{space 2} .0094163{col 37}{space 1}   13.10{col 46}{space 3}0.000{col 54}{space 4} .1048815{col 67}{space 3} .1417926
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0279556{col 26}{space 2} .0073201{col 37}{space 1}    3.82{col 46}{space 3}0.000{col 54}{space 4} .0136084{col 67}{space 3} .0423027
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0113497{col 26}{space 2} .0225249{col 37}{space 1}    0.50{col 46}{space 3}0.614{col 54}{space 4}-.0327983{col 67}{space 3} .0554976
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0332351{col 26}{space 2} .0233281{col 37}{space 1}   -1.42{col 46}{space 3}0.154{col 54}{space 4}-.0789573{col 67}{space 3} .0124871
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0053143{col 26}{space 2} .0280479{col 37}{space 1}    0.19{col 46}{space 3}0.850{col 54}{space 4}-.0496587{col 67}{space 3} .0602873
{txt}{space 7}urban {c |}{col 14}{res}{space 2} .0273199{col 26}{space 2} .0094838{col 37}{space 1}    2.88{col 46}{space 3}0.004{col 54}{space 4}  .008732{col 67}{space 3} .0459078
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2}-.2404971{col 26}{space 2} .0302944{col 37}{space 1}   -7.94{col 46}{space 3}0.000{col 54}{space 4}-.2998731{col 67}{space 3}-.1811211
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.859844{col 26}{space 2} .0953256{col 37}{space 1}  -19.51{col 46}{space 3}0.000{col 54}{space 4}-2.046679{col 67}{space 3}-1.673009
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2}-1.084331{col 26}{space 2} .0902608{col 37}{space 1}  -12.01{col 46}{space 3}0.000{col 54}{space 4}-1.261239{col 67}{space 3}-.9074235
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .1327437{col 26}{space 2} .0894745{col 37}{space 1}    1.48{col 46}{space 3}0.138{col 54}{space 4}-.0426231{col 67}{space 3} .3081106
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.523235{col 26}{space 2} .0902493{col 37}{space 1}   16.88{col 46}{space 3}0.000{col 54}{space 4} 1.346349{col 67}{space 3}  1.70012
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1255717{col 26}{space 2} .0325788{col 54}{space 4} .0755184{col 67}{space 3} .2088003
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 939.29{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. 
. 
. meoprobit my_edu class income employer high_skilled education age gender marital religion urban i.year if ideology==1||country:, nolog
{res}
{txt}Mixed-effects oprobit regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,301
{txt}Group variable: {col 25}{res}country{col 49}{txt}Number of groups{col 67}={res}{col 69}        34

{col 49}{txt}Obs per group:
{col 63}{txt}min{col 67}={res}{col 69}         7
{col 63}{txt}avg{col 67}={res}{col 69}     303.0
{col 63}{txt}max{col 67}={res}{col 69}     1,064

{txt}Integration method: {col 21}{res}mvaghermite{col 49}{txt}Integration pts.{col 67}={col 78}{res}7

{col 49}{txt}Wald chi2({res}11{txt}){col 67}={res}{col 70}   321.71
{txt}Log likelihood = {res}-11955.371{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      my_edu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}class {c |}{col 14}{res}{space 2} .0373358{col 26}{space 2} .0075626{col 37}{space 1}    4.94{col 46}{space 3}0.000{col 54}{space 4} .0225134{col 67}{space 3} .0521582
{txt}{space 6}income {c |}{col 14}{res}{space 2}-.0005308{col 26}{space 2} .0110086{col 37}{space 1}   -0.05{col 46}{space 3}0.962{col 54}{space 4}-.0221073{col 67}{space 3} .0210456
{txt}{space 4}employer {c |}{col 14}{res}{space 2}-.0741839{col 26}{space 2} .0444091{col 37}{space 1}   -1.67{col 46}{space 3}0.095{col 54}{space 4}-.1612241{col 67}{space 3} .0128563
{txt}high_skilled {c |}{col 14}{res}{space 2} .0349683{col 26}{space 2}  .025942{col 37}{space 1}    1.35{col 46}{space 3}0.178{col 54}{space 4} -.015877{col 67}{space 3} .0858137
{txt}{space 3}education {c |}{col 14}{res}{space 2} .1191099{col 26}{space 2} .0099617{col 37}{space 1}   11.96{col 46}{space 3}0.000{col 54}{space 4} .0995854{col 67}{space 3} .1386344
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0278153{col 26}{space 2} .0073667{col 37}{space 1}    3.78{col 46}{space 3}0.000{col 54}{space 4} .0133768{col 67}{space 3} .0422537
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0069231{col 26}{space 2} .0226256{col 37}{space 1}    0.31{col 46}{space 3}0.760{col 54}{space 4}-.0374222{col 67}{space 3} .0512684
{txt}{space 5}marital {c |}{col 14}{res}{space 2}-.0326253{col 26}{space 2} .0233377{col 37}{space 1}   -1.40{col 46}{space 3}0.162{col 54}{space 4}-.0783663{col 67}{space 3} .0131157
{txt}{space 4}religion {c |}{col 14}{res}{space 2} .0058626{col 26}{space 2} .0280508{col 37}{space 1}    0.21{col 46}{space 3}0.834{col 54}{space 4} -.049116{col 67}{space 3} .0608412
{txt}{space 7}urban {c |}{col 14}{res}{space 2}   .02621{col 26}{space 2}  .009499{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} .0075924{col 67}{space 3} .0448277
{txt}{space 12} {c |}
{space 8}year {c |}
{space 7}2019  {c |}{col 14}{res}{space 2} -.243052{col 26}{space 2}  .030364{col 37}{space 1}   -8.00{col 46}{space 3}0.000{col 54}{space 4}-.3025643{col 67}{space 3}-.1835398
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/cut1 {c |}{col 14}{res}{space 2}-1.880956{col 26}{space 2} .0967109{col 37}{space 1}  -19.45{col 46}{space 3}0.000{col 54}{space 4}-2.070506{col 67}{space 3}-1.691406
{txt}{space 7}/cut2 {c |}{col 14}{res}{space 2} -1.10482{col 26}{space 2} .0917004{col 37}{space 1}  -12.05{col 46}{space 3}0.000{col 54}{space 4} -1.28455{col 67}{space 3}-.9250907
{txt}{space 7}/cut3 {c |}{col 14}{res}{space 2} .1128012{col 26}{space 2} .0908886{col 37}{space 1}    1.24{col 46}{space 3}0.215{col 54}{space 4}-.0653373{col 67}{space 3} .2909396
{txt}{space 7}/cut4 {c |}{col 14}{res}{space 2} 1.503546{col 26}{space 2} .0916376{col 37}{space 1}   16.41{col 46}{space 3}0.000{col 54}{space 4}  1.32394{col 67}{space 3} 1.683153
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}country     {col 14}{c |}
   var(_cons){c |}{col 14}{res}{space 2} .1259368{col 26}{space 2} .0326782{col 54}{space 4} .0757324{col 67}{space 3} .2094226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. oprobit model: {txt}{help j_chibar##|_new:chibar2(01) =}{res} 936.78{col 55}{txt}Prob >= chibar2 = {res}{col 73}0.0000

{com}. 
. estimates store edu_4

. 
. 
. 
. coefplot (edu_3) (edu_4) , eform drop(_cons) omitted xline(1, lcolor(black) lwidth(thin)) lpattern(dash) graphregion(fcolor(white)) mlabel format(%9.2f) mlabposition(8) mlabsize(tiny) level(95) keep (class) 
{res}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Han\Desktop\document\불평등 연구\주제14_내로남불_교육\social_sciences\data\analysis_2.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}10 Nov 2024, 13:19:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}